This is the third in our series on DeFi curation, following our analyses of setting LTVs and caps for collaterals and the relevance of JIT MEV. We develop a quantitative framework for estimating liquidation sizes and their implications for risk parameter calibration in DeFi lending. Using over 6,400 liquidation events from Aave v3, we model the ratio of liquidation size to pool depth on the log scale and find it is best captured by a three-component Gaussian Mixture Model (GMM). This approach explains anomalies introduced by liquidator behaviour, most notably round-number clustering around $1,000. We show that while typical liquidations are nearly constant in dollar terms across pool sizes, the tails deviate, requiring conservative treatment. To address this, we impose a minimum floor on expected liquidation sizes, ensuring robustness for smaller or under-sampled pools. Our estimates indicate that for pools larger than $1 billion, liquidation events will remain below 6 basis points with 99% certainty and below 32 basis points with 99.9% certainty. For smaller pools, we extrapolate the values from the $1 billion pool size, offering a conservative and tractable framework for stress testing and setting collateral risk parameters.

Introduction

Understanding the mechanics behind asset onboarding decisions provides valuable insight into risk management. In our previous work, we outlined how to use historical data to estimate the price slippage, $$S$$, for a given liquidation volume, $$L$$. We now focus instead on the conditional distribution of liquidation size given pool depth $$f_{L|D}(\ell|d)$$. Directly estimating this density is difficult, so we instead work with the normalised liquidation ratio $$R=\frac{L}{D}$$ whose density is related by $$f_{R|D}(r|d) = d\cdot f_{L|D}(rd|d)$$.

Inverting the CDF allows us to estimate the expected slippage $$\mathbb{E}[S | L < X, D]$$ where $$X = F_{L|D}^{-1}(0.99|d)$$ is the 99th percentile of liquidation volumes for a given pool depth $$D$$.

Data overview

We analysed 6400 liquidation events from Kaiko's Aave v3 dataset, filtering for:

• Minimum liquidation size of $10 (removing dust)

• USD-denominated debt assets (USDT, USDC, DAI, etc.)

In this analysis we discarded Compound data because it had an FX issue between cTokens and the underlying debt asset that couldn't be easily resolved. We will likely redo the analysis in the future when that is rectified.

Sources

Columns taken from two of Kaiko's endpoints: /lending.v1/events for liquidation data and /lending.v1/snapshots for snapshotting the total pool liquidity to get the following columns:

• blockchain

• protocol

• address (pool address)

• liquidation_debt_asset_symbol

• transaction_hash

• total_liquidity (deposits)

• liquidation_debt_amount_in_asset (liquidation amount)

We grabbed data for blocks with liquidation events only and created a multi-index on blockchain, protocol, address, liquidation_debt_asset_symbol, transaction_hash with the values being total_liquidity from snapshots and liquidation_debt_amount_in_asset from events.

Data preparation

• Liquidation Ratio: debt_amount / total_liquidity

• Depletion Percentage: (initial_liquidity - min_liquidity) / initial_liquidity

Note that if you use the Kaiko dataset, please be aware that liquidations need to be normalised into USD, do not simply divide liquidation_debt_amount_in_asset / total_liquidity - an easy workaround is to imply the FX rate from some existing fields:

# Calculate implied price (amount is in tokens and liq is in USD)
df['implied_price'] = df['liquidation_debt_amount_in_asset'] / df['amount']

# Convert total_liquidity to USD
df['total_liquidity_usd'] = df['total_liquidity'] * df['implied_price']

# Calculate correct liquidation ratio
df['liquidation_ratio'] = df['liquidation_debt_amount_in_asset'] / df['total_liquidity_usd']

Distribution of liquidation as a ratio of pool size

It is immediately clear that the data follows a log distribution but there are also interesting anomalies - notably the left of the peak in the transformed dataset.

Interestingly if we look at the distribution of absolute USD amounts in this region of -14 to -16 we see a strange pattern!

Plotting for all data, it becomes apparent that this due to liquidator preference for round numbers. When looked at in absolute terms, the liquidations show several spikes at different dollar thresholds. Most notably $1k which was hugely dominant in the wstETH liquidations.

As a side remark, running a liquidation bot to liquidate at $990 instead of $1k might be highly profitable!

This has implications for estimation of distributions because the fits tend to be incorrectly trying to fit this spike which is more related to liquidators rather than liquidations themselves. This also means that historical data kernels would likely misrepresent the tail risk.

The models are largely behaviour like gaussians for example, a huge degree of freedom on a t-model converges it to a normal distribution. We can look instead at a Gaussian Mixture Model to model the "hole" that is left around $500-1k as liquidators leave opportunities on the table despite health deterioration.

We start the solver with two gaussians at -18 and -16 straddling the "hole" and one where the true mean appears to reside at -13 and obtained a significantly improved fit.

For a given pool depth $$D=d$$, we can now calculate $$X = F_{L|D}^{-1}(0.99 | d) = d\cdot F_{R|D}^{−1}​(0.99|d)$$ where $$F_{R|D}^{-1}$$ is the inverse CDF (quantile function) of the GMM distribution. This gives us the 99th percentile liquidation volume conditional on pool depth.

We estimated the following percentiles analytically:

• 99.9th: -5.758

• 99th: -7.448

• 95th: -9.030

which gives the following estimates for $$\ln{F_{R|D}^{−1}​(y|d)}$$ so when exponentiated we have (in basis points):

• $$X/d = 31.6$$ with 99.9% confidence

• $$X/d = 5.8$$ with 99% confidence

• $$X/d = 1.2$$ with 95% confidence

Practically this means for a pool size of $10m you expect liquidations of just under $6k with 99% certainty.

Liquidation ratio as a function of pool size

We also investigated whether the size of pool made a difference in liquidation to test the hypothesis that borrowers will tend to come in for a specific minimum size so we expect that small vaults will see a higher ratio of liquidation ratios. We expect that the liquidation ratio will decrease exponentially with respect to the pool size.

This would alter the expectation framework: rather than assuming a stationary conditional density $$f_{R|D}$$​, we should incorporate the power-law scaling $$R \sim D^{\beta}$$ and would change the quantile function to become $$X(d) \approx d^{\,1+\beta}\cdot F_{\tilde R}^{-1}(p)$$

The size of AAVE presents some uncertainty issues at lower sizes but overall the regression seems to hold relatively stable.

On the log–log fit we get $$log_{10} R=\,-0.952\,\log_{10} D + 3.379$$ so the liquidation ratio scales almost inversely with pool size $$R \propto D^{-0.95}$$. Equivalently, the dollar size of a typical liquidation is nearly flat across depth at approximately $6k. Similarly, we find a lower bound of liquidation size to be defined by the line $$\log_{10}R=2-\log_{10}D$$ which we fitted heuristically. This aligns with the approximate cost of gas on AAVE since liquidations that are unprofitable will not occur due to flash mechanics.

The estimated slope $$\beta \approx -0.95 \pm 0.04$$ implies that liquidation ratios decline nearly inversely with pool size, such that dollar liquidation volumes remain almost constant across depths.

Can we ignore dependence on pool size?

tldr no.

The standard error in the slope is 0.04 so it's within practical reason to estimate the gradient as -1.

From a modelling perspective, treating the gradient as $$\beta \approx -1$$ has clear implications. It indicates that liquidation events are executed in fixed-dollar lots, rather than as a fixed percentage of pool depth, implying that the conditional distribution $$f_{L|D}$$ scales sub-linearly with $$D$$. Formally, if $$\log R = \beta \log D + c$$, then liquidation sizes scale as $$L \propto D^{1+\beta}$$. With $$\beta \approx -1$$, this reduces to $$L \approx \text{constant}$$, reinforcing the interpretation that liquidations are bounded primarily by transaction cost economics and bot incentives rather than pool-specific dynamics. For risk calibration, this means that extrapolating liquidation risk across depth should account for a nearly depth-independent stress size, with deviations from $$-1$$ capturing only second-order effects. In practice, this provides justification for applying fixed-dollar stress tests across pools of different sizes, while reserving depth-scaled adjustments for the tail quantiles where empirical departures from the idealised $$-1$$ slope become material.

While the regression shows that liquidation ratios scale nearly inversely with depth $$\beta \approx -0.95$$, the practical implications differ depending on pool size.

When fitting the absolute dollar liquidations we see artefacts that perhaps that ratio captures the tail behaviour more accurately... see the tail distribution seems to underperform in the extreme for absolute dollar numbers.

Confidence levels with size uncertainty

For small pools (<$50M), the flat $6k-lot structure implies disproportionately large liquidation ratios, however, these pools are not systemically important: their small liquidity base makes them less relevant to slippage-driven spirals that threaten protocol stability.

Given the nature of the dataset being strongly biased to $1bn + size pools it's possibly prudent to make a conservative assertion that the approximations of liquidation size are only valid for pools of >1bn.

For smaller pools, we impose a conservative floor by taking the minimum liquidation size to be that of a $1bn pool $$\frac{X}{d} \;\;\geq\;\; \left(\frac{X}{d}\right)_{\text{1bn}}$$ where $$\left(\frac{X}{d}\right)_{\text{1bn}}$$ is the loss ratio inferred from a $1 bn pool.

Thus, the floored expected liquidation is

$$\frac{X}{d} \;\;=\;\; \max\!\Biggl\{\,\widehat{\frac{X}{d}}(S),\;\left(\frac{X}{d}\right)_{\text{1bn}}\,\Biggr\}$$

where $$S$$ is the pool size, and

$$\left(\frac{X}{d}\right)_{\text{1bn}} \;=\;\begin{cases}31.6 & \text{with 99.9\% confidence}, \\[6pt]5.8 & \text{with 99\% confidence}, \\[6pt]1.2 & \text{with 95\% confidence}.\end{cases}$$

Conclusion

We studied 6,400+ Aave v3 liquidations and modelled liquidation size as a ratio of pool depth $$R=L/D$$. On the log scale, the body of $$\ln R$$ is close to Gaussian but exhibits structure driven by liquidator behaviour, most visibly a round‑number spike around $1k and a “hole” just below that threshold. A 3‑component Gaussian Mixture Model (GMM) on $$\ln R$$ captures this behaviour and materially reduces tail error relative to single‑family fits.

We found typical dollar liquidation sizes are nearly flat across depth (≈$6k in our sample), while tail quantiles grow sub‑linearly with $$D$$.

For risk calibration, compute $$X_p(D)$$ from the GMM and pair it with a venue‑specific slippage curve to estimate $$\mathbb{E}[S\mid L\le X_p(D),D]$$. For small or poorly represented pools, apply a minimum‑pool size floor of $1 billion.

We estimate the max single liquidation size, with 99% certainty, on pools smaller then $1 billion to be around $580k and scaling with sizes over $1 billion as 0.058% of the pool in dollar terms.

Future Work

This framework opens several directions for refinement and validation. First, expanding coverage beyond Aave v3 to include Aave v2/v1 and Compound will test the portability of the fixed-lot interpretation across protocols. For Compound in particular, resolving the FX treatment of cTokens is essential to establish a comparable baseline.

Second, the methodology should be validated out-of-sample with newer data and across different market regimes. This will help identify whether the observed GMM structure is stable or if liquidation dynamics shift as gas costs, MEV opportunities, or liquidator strategies evolve. Such stress testing will clarify whether the fixed-dollar clustering remains robust or drifts with infrastructure changes and arbitrage competition.

Third, greater granularity in borrower and pool-level features such as: concentration risk; asset volatility; or liquidity fragmentation; would allow conditioning the mixture model on explanatory variables, rather than treating all liquidations as exchangeable. This would move the framework from a descriptive fit toward a predictive tool that distinguishes systemic pools from idiosyncratic ones.

Finally, incorporating this distributional framework into a full risk calibration pipeline, combining liquidation size distributions with venue-specific slippage models, will allow systematic derivation of supply caps and LTV limits. This end-to-end integration is the natural next step toward making liquidation modelling a standard component of protocol-level risk management.

Do Just-In-Time (JIT) liquidity or Miner Extractable Value (MEV) strategies materially affect trading in Avalanche’s largest pool (wAVAX/USDC) ?

Why This Matters for Curators

For curators setting risk parameters, the presence of JIT liquidity or MEV is extremely important for modelling

• On some chains, opportunistic liquidity providers or arbitrageurs jump in to smooth price impact, changing the effective slippage profile.

• If that were the case on Avalanche, we might need to factor in centralised exchange (CEX) order flow or external arbitrage support.

If these effects are absent, the modelling is much more straightforward: what you see on-chain is what you get. However, the lack of these effects can have profound impacts on the vault capacity.

Full Dataset Analysis

We began with a large dataset of 1.26M transactions from LFJ’s wAVAX/USDC pool. From this, we identified:

• 17,124 JIT-like events (≈1.4% of txs)

• 153,371 MEV-like events (≈12.1% of txs)

We then regressed signed transaction volumes against prior activity, looking for the tell-tale diagonal “structure” that appears when JIT or MEV strategies are active.

Result

• JIT: Spearman ρ ≈ 0.02, (R^2 ≈ 0.0004)

• MEV: Spearman ρ ≈ 0.10, (R^2 ≈ 0.015)

Both relationships are statistically detectable thanks to the huge sample size, but economically negligible. In other words, the scatter plots look like Gaussian noise — no exploitable structure.

High-Value Transactions (>$10k)

To stress test further, we isolated only:

• Correct-side JIT deposits (250 txs)

• Same-direction MEV swaps (9,049 txs)

Here again the signal was weak:

• JIT: no significant correlation (ρ ≈ −0.05, p = 0.43)

• MEV: statistically significant ρ ≈ 0.33, but still small in economic terms R^2 ≈ 0.05

This seems to confirm that even in larger trades, Avalanche pools aren’t seeing consistent JIT or MEV arbitrage flows.

What This Means

There is no evidence of traders moving capital from centralised exchanges “bailing out” large trades with arbitrage support, rather it seems to suggest that trading is done on a slower-moving inventory rebalancing basis. To confirm this we would ned CEX data and calculate the autocorrelation of bin movements with respect to price but that is beyond the point of this analysis.

From a curator’s perspective, this is helpful: models of slippage and liquidation risk can be built directly from observed on-chain liquidity.

We don’t need to supplement our analysis with Binance or other CEX data when setting vault parameters.

Lack of JIT also presents a slight complexity because the absence of JIT means we should possibly separately consider other AMMs like Blackhole & Dodo as additive liquidity sources rather than assuming they will have commingled impact.

Their liquidity is non-trivial and can move liquidation costs down, but for now LFJ's exchange mechanics still dominate at scale due to the quantisation of the bin mechanics so we will continue to primarily focus on LFJ.

Final Thoughts

The absence of JIT/MEV is an opportunity for trading - both JIT and MeV are particularly crowded on Ethereum mainnet.

Yet, as of 24th August 2025 AVAX/USDC trades just under $1bn 24h total volume and >$70m on LFJ alone. meaning the CEX-DEX arbitrage opportunity is approximately half of that of Pendle's total x-chain volume on this single pool.

Maybe there is in an error in the data, as it's unclear why traders are fighting so hard over mainnet and ignoring such an opportunity.

Curators seemingly rarely publish their quantitative analysis publicly, but understanding the mechanics behind asset onboarding decisions provides valuable insight into risk management. We hope to document the process of bringing new assets onboard with modelling and market microstructure analysis.

This is an attempt at a more rigorous approach to onboarding new assets - specifically the "LTV Limits" (LLTVs) and "Supply Caps" (Caps).

tldr we’re using historical data to model how much $wAVAX might drop in a single block given a specific volume of sells. We'll measure the size of liquidations for various pools and estimate the expected max sell-off for a given vault capacity. We can then calculate the slippage we might get and use that to infer our LTV ratio and Vault capacity params.

The core objective is to estimate the price slippage $$S$$ for a given liquidation volume $$L$$ which is written as $$\mathbb{P}(S \leq s \mid L = \ell)$$ and it's conditional probability density $$f_{S|L}(s|\ell)$$. Then, we will calculate the expected slippage, $$\mathbb{E}[S \mid L < X, D]$$ where $$X$$ is a percentile of liquidation volumes $$X = F_L^{-1}(0.99 \mid D)$$ for a given pool depth $$D$$, or determined via scenario analysis based on liquidation sizes from other platforms.

Trader Joe (LFJ) Venue Data

The analysis proved more complex than initially anticipated due to numerous unusual market characteristics. Trader Joe is not a well documented trading venue and has several idiosyncrasies that create great benefits for users but also great doom-loops for analytics. Notably, the data revealed instances of large trades that moved markets 6% in favour of traders - a phenomenon that defies conventional market mechanics. The following examples illustrate these data anomalies.

Another anomaly revealed what appears to be probability wave interference effects in the exchange, similar to quantum mechanics (except not in infinite dimensional Hilbert space!). In LFJ they have discretised (quantised) the liquidity bins with specific 10 basis point blocks and the result is a variety of harmonics around the "ground state". This unexpected phenomenon showed interference patterns that deviated dramatically from traditional "bell shaped" return distributions.

This analysis covers wAVAX / USDC, representing the most liquid DEX pair on Avalanche. The dataset encompasses the entire history of swaps until the previous evening, with block, $$b$$, returns calculated by $$r(b) = \text{VWAP}(b) - \text{VWAP}(b-1)$$ using Swap events from HyperSync. The exact cause of this strange return distribution remains unclear, but this anomaly alone delayed the wAVAX pool go-to-market by approximately 2-3 days due to the additional analysis required.

To validate the interference theory hypothesis, we modelled a Poisson distribution with triangular convolution of multiple swaps per block causing interference. This approach looked similar to the empirical observations (subject to proper parameter fitting), enabling a resemblance of sanity.

With a robust model of price movements for various sell sizes on the primary venue for wAVAX/USDC trading, stress testing under various market conditions becomes possible.

Kyle's Lambda

Kyle's Lambda is a core concept in market microstructure, measuring how trades affect prices in financial markets. It helps estimate execution costs, particularly for high-frequency trading (HFT) in single-state executions. The model uses regression to quantify price changes based on trade volume, expressed as $$r_b = \lambda \cdot \sqrt{|V_b|} \cdot \text{sign}(V_b) + \epsilon_b$$, where for a given block, $$r_b$$ represents slippage, $$\lambda$$ is the price impact coefficient, $$V_b$$ is the signed trade volume, and $$\epsilon_b$$ is the random noise term.

The returns were calculated as the change in VWAP between consecutive transactions $$r_{x} = \text{VWAP}_x - \text{VWAP}_{x-1}$$ where $$x$$ denotes the transaction index.

Impact of Slippage

The analysis revealed that atomic liquidations can be performed with minimal slippage on collateral assets. While general collateral degradation may occur, the likelihood of generating bad debt through the liquidation process itself remains very low.

The data demonstrates that liquidating $40,000 worth of $wAVAX from an unhealthy position typically results in only $12 slippage on the AMM. This finding proved surprising and requires further individual transaction analysis, though the data seems to consistently support this conclusion.

The bin-model architecture of the LFJ exchange appears responsible for this favourable slippage profile. Analysis uncovered instances of $600,000 orders executing with zero slippage. While LFJ's pricing can appear confusing to users due to dust-related price distortions that may not reflect actual liquidity, the Volume Weighted Average Price (VWAP) typically provides fair execution.

Next Steps

In the next post we will dive into liquidity data and analyze liquidation sizes across DeFi vaults. We’ll explore Just-In-Time (JIT) liquidity patterns, testing the hypothesis of JIT activity and then analyse the liquidation sizes for various DeFi vaults.

My kids often say: "But, Why?"

If you ask this question about 3-4 times you either uncover irrational assumptions or quantum physics (or both!).

Why do central banks target 2% ? The answer may shock you...

Central banks target 2% because the Bank of New Zealand arbitrarily picked 2% Literally because it was higher than 1% ... and everyone copied them!

There is no real fundamental analysis that underpinned the original decision that every major central bank copied.

Maybe they got it right? Boris Johnson had a famous phrase^ for civil servants that are paid lots of money to take on no risk with no accountability or engagement,

"great supine protoplasmic invertebrate jellies"

I prefer his assessment.

LTV Limits

I was looking at how to set LTV limits - basically everyone seems to have copied each other +/- a bit. I haven't yet found any analysis backing a lot of the LTVs on most platforms... at least not publicly and nothing quantitative.

This got me thinking, shall we just take 80/85% for $wstETH and 70/75% for $wAVAX ... or is this just sloppy?

In traditional finance risk modelling, this is one of the most regulated areas because it's one of the most risky for the financial system. The Standard Initial Margin Model (SIMM) that arose from the Fundamental Review of the Trading Book (FRTB) Sensitivities-Based Approach (SBA) approach became the standard of interbank margin modelling. It levied heavily on the Value at Risk model (VAR / Expected Shortfall).

Let us firstly define a separation of two types of assets

• native assets: UNI, ETH, BTC - these assets have no off-chain credit risk

• non-native - assets are deposited and taken off-chain

Even this basic classification puts us in a conundrum: Tether and Circle literally take assets off chain and deposit into non-cash like assets.

Circle assures market after stablecoin USDC breaks dollar peg

Stablecoin USD Coin (USDC) lost its dollar peg and slumped to an all-time low on Saturday before recovering most of its losses after Circle, the firm behind it, assured investors it would honor the peg despite exposure to failed Silicon Valley Bank.

https://www.reuters.com

For now, however, we will treat $USDC and $USDT as special cases of "native assets" (eeek!) until they get a banking license. This however remains such a systemic risk it will have to be accepted as a fundamental tenant of risk management on-chain. Either accept it or don't play.

For the native assets, VaR based models with credit risk add-ons are probably the correct approach due to the nature of their operations, the credit components and the redemption windows.

This approach isn't so valid for "native assets" - VaR is not the right measure of risk in a context where no (acknowledged) credit exposure exists, particularly in liquidations where zero-holding period is usually taken. Generally the "inventory risk" is pushed to JIT liquidity providers who will increasingly start to look at statistical arbitrages (requiring holding periods) as the instant "free lunch" starts to dry up with highly sophisticated actors like Jump Crypto re-entering.

So what is the correct model for liquidators? A model that measures the likelihood of these arbitrageurs to come and backstop the trade made. This cannot be judged from the LP pools as most of it is "dark".

We have to measure Execution Risk (Slippage).

Modelling Execution Risk

As mentioned, the AMM model is sort of irrelevant with most liquidity these days off-exchange. The reality is that we should inherit directly from quant risk modelling as most of the liquidity will indeed come from Limit Order Books (LOBs). 

When creating any model in quant finance the dumbest thing to do is go to machine learning (unless you just finished a PhD on the topic) - instead start with something and build on it.

It is with this in mind that we look to basic price impact models such as Kyle's Lambda: The foundation of market microstructure.

Kyle's Lambda

Kyle (1985) formulated a framework including a parameter known as a Kyle's Lambda. This parameter comes from a modelling the optimal behaviours of of informed traders amongst the uninformed. Assume you are an insider and "informed" then what is the optimal strategy to move your position? Kyle considers this topic in the context of a single static trade. For more complex sequential trading actions you must consider optimal inventory and additional constraints (see below).

For a DeFi interpretation, Kyles Lambda can be interpreted as the cost of requiring a certain market depth in a given period of time which is established by relating the price change during a transaction $$r$$, with the volume, $$V$$ executed subject to a random process $$\epsilon$$ which is assumed to be independently and identically distributed,

$$r = \lambda V + \epsilon$$

like everything so simple, it's a bit of a pain to derive but super easy to explain conceptually.

In this approach I decided to remove the "signed volume" approach of Hasbrouck (2006) since we can precisely obtain all the orders on-chain in a separate fashion. This means we can calculate Kyles lambda in two directions, buys & sells.

However, we only care about sells for liquidations.

Now we have a regression model to run across all the data on the AMMs.

Modern "solvers" like Odos (below) route single transactions through a large number of liquidity venues. It might present a challenge to identify correctly all the events from all venues.

Future Model Improvements

There are so many extremely talented quants working in this space it's hard to know where to start but I'll just pick two of my personal favourites.

On the single trade horizon, Bouchard in "Trades, Quotes, Prices" looks at correlator models which I found fascinating from a physics perspective. He demonstrates how to calculate correlators that accurately fit CEX execution data in crypto but I think it could probably be used in the same way on interest rate models to identify the reaction speed.

For multi-trade inventory management it becomes more of an optimal inventory management problem that market makers face, this is tackled by Cartea quite comprehensively in "Algorithmic and High Frequency Trading".

The Future of DeFi Quant Risk?

ISDA is a global co-operative involving all major banks. Effectively ISDA is a DAO. I hope that curators should look to create something similar to ISDA for risk modelling such that users have transparency with a simple benchmark.

References

• BIS: Fundamental Review of the Trading Book

• Kyle, A. S. (1985). Continuous auctions and insider trading. Econometrica: Journal of the Econometric Society, pages 1315–1335.

^Johnson was expected to face a two-hour grilling over his proposed £16.5 billion budget, which included cuts to services like the fire and police departments. The insult was a response to the assembly's decision to bypass the question session, which Johnson perceived as a lack of engagement or accountability.

Why Gold Became Currency's Benchmark

What makes a good currency? Economists point to three key roles: 

1. A unit of account (for pricing goods)

2. Medium of exchange (for transactions)

3. Store of value (for holding wealth)

Gold has met these needs for ages. It shares features with today's digital assets. Its scarcity comes from geological rarity and first appeared in items like jewellery. Immutability means it keeps its worth even if melted down. Fungibility lets it be reshaped: coins can be split or reformed without losing value.

This mix beat out other options. Diamonds are scarce and immutable but hard to divide. Tulips or feathers are scarce and fungible but wear out or rot over time. Rocks are immutable and fungible but far too common. Gold's traits allowed decentralised pricing. It enabled trustless trades without banks, similar to blockchain now. Think of barter's problems, like arguing over Pokémon cards in school. A shared asset simplifies this by creating a standard unit for exchanges. This is why currencies are important, they remove arguments from cross-asset pricing by simplifying into a base unit of exchange.

Gold fit the market well and gained traction. Prices peaked around 1400 as demand for coins grew, based on records adjusted to 2024 USD purchasing power. However problems with the system soon emerged...

Gold's Downfall: Supply Floods and Everyday Limits

Gold's supply is not fixed. The discovery of the Americas in the late 1400s flooded markets with the precious metal, which drove prices down to about $1,000 in adjusted terms. Additionally, gold was difficult to use at scale due to its heaviness and scarcity, making it challenging to transact at large volumes.

China's Ming Dynasty is a good example of this in the 15th Century. Taxes were collected as a proportion of crops, paid to the emperor. Naturally, farmers gave the bad crops as tax. The empire tried gold briefly but found it too rare for its huge population, which accounted for a quarter of the world's total at the time. Silver offered a better balance and became the official currency in the 16th century. But as demand rose, silver supply started to dry up. As a result, the Ming Dynasty soon found itself in a position where they could no longer pay their own soldiers due to Silver shortages. 

Help arrived from early global trade. The Spanish found Potosí in Bolivia, the world's richest silver site. This solved China's shortage. From 1500 to 1800, Bolivia and Mexico produced 80% of global silver. About a third ended up in China. Economists call this the start of true international trade.

The Spanish amassed incredible wealth and squandered it on a massive meme coin-like trade of invading England (massive upside gamble), the Spanish Armarda 1588. Much like the Solana Trenchers of today, the Spanish learned the hard way that going all in is a very risky business.

As a result Imperio Español lost their money and influence and remained pissed to this day, taking up football to stick it to the English every 4 years. 

Silver rose as a global currency, pushing gold aside. New South American gold finds added to the supply glut. Prices fell during the Age of Discovery before steadying in the 17th century.

The Ming example showed the need for balance in scarcity, immutability, and fungibility. As trust in banks grew, money relied less on physical coins. It also reduced the need for the Crown to back lower-value metals like silver.

The Rise and Fall of the Gold Standard

By the 18th century, gold lost ground. The Bank of England, founded in 1694, tested paper notes backed by gold. However, problems arose when  the stubborn Isaac Newton, Master of the Royal Mint overvalued gold in 1717 shortly before being a dick to Leibniz.

This led to Portuguese gold flooding England and market distortions. Traders bought cheap silver in Britain, melted it, and sold it in Asia and Europe. The Bank of England had swallowed up silver via Newton's erroneous conversion calculator for the Silver/Gold rate and was a major reason for Silver to vanish as a global currency.

The Industrial Revolution sped the decline. It started in 1771 at Arkwright's mill in Derbyshire. New technologies made gold expensive to hold compared to growing goods and services. People invested in companies instead, and gold's price stalled. Newton's mistake drained silver stocks, helping end its global use.

The Gold Standard was short-lived, despite its reputation. The Bank of England set it in 1816, but it lasted only until 1914. Victorian calm was rather artificial, supported by empire, not natural stability. Then in World War I, the great European Empires sold off their reserves to cover debts, crashing prices like a modern asset dump. This is the same as a shitcoin post-TGE unlock. The price of gold collapsed. 

The price for the stupidity of Kings, had been paid by blood and gold. 

The Great Depression led to Roosevelt's 1933 gold seizure. This devalued the dollar by loosening gold ties, allowing more money printing. This wasn’t a rise in gold price per-se. Less gold was required to back the dollar, and the FED was free to print.

It was the OG brrrrr.

In 1944, Bretton Woods linked the dollar to gold at $35 per ounce. Other nations tied their currencies to the dollar. This created a gold-exchange system and boosted US power. The US Empire was born, backed by nukes and the Dollar. This valuation of gold was treated as sacrosanct and a modern “Gold Standard” despite its primary purpose being brrrrrrrr.

However, Nixon then invoked some Roosevelt in 1971 by dropping the gold standard entirely and the price of gold spiked dramatically as currency was debased. The Fiat Currency era started and frankly became the most volatile period of all time for the price of gold. Gold really did trade like a shitcoin in the 2000s fluctuating as much as 10x in the space of 10 years; and certainly not upwards!

Gold's Hidden Volatility and Lessons for Bitcoin

The chart covers 1264 to 2024 in adjusted USD and shows the ups and downs. Medieval prices were often higher in real terms than today. The Black Death from 1347 to 1351 caused huge economic shifts through labour shortages and inflation. Discoveries cut values in half. Industrial times brought drops as output grew faster than metal. The Gold Standard era from 1717 to 1971 hid volatility under rules. Fiat era saw wild moves, with rises in crises like COVID-19 but overall loss in buying power.

As of July 2025, gold is at about $3,376 per ounce nominally. This is a high driven by global tensions and inflation worries. Adjusted for a 2.7% CPI increase from mid-2024, it equals roughly $3,287 in 2024 USD. This continues the chart's fiat volatility.

Bitcoin follows a similar pattern. It faces criticism for volatility, with one-year swings around 50%, compared to gold's 12 to 20%. But gold in the 1970s post-standard had spikes over 30% during changes. Research shows Bitcoin acts like gold as a hedge but is more volatile due to its newness and speculation. Both rely on scarcity: Bitcoin's 21 million limit matches gold's limits. Yet both face supply and demand jolts. Gold's past hints Bitcoin could steady over time, but neither promises constant value.

Nixon's move showed gold's weakness as a store. Trust, not the metal, supports trade. Central banks have done okay, but the 2008 crash eroded trust in the modern era. This fuels fears of inflation, especially in developing markets.

Shattering Gold's Stability Myth

Gold's journey reveals the weakness in its 'store of value' image. Centuries of volatility match Bitcoin's ups and downs. In 2025, with prices at highs, it reminds us that trust is money's core. As DeFi calls, using its strengths and learning from gold could build a stronger financial system.

DeFi: The Next Step in Trustless Money

DeFi offers a fix through programmable money via smart contracts. These create trustless, decentralised systems like gold's early appeal. Cryptocurrencies provide scarcity, immutability, and fungibility in digital form. But challenges remain. Smart contracts can be hacked, and compliance issues let bad actors dodge rules, worrying governments and regulators.

DeFi is the future with trustless contract-based systems. But rogue actors still prey on crypto users. Hackers and exploiters make headlines at retail's expense. Despite low real metrics, the potential for criminals and rogue states to evade sanctions terrifies those in power, who've long used finance as a substitute for war.

Smart contract risk + Compliance risk = the two biggest blockers to adoption.

At Keyring, we believe DeFi will win. Cryptocurrencies satisfy all the requirements that gave such strong PMF to historic currencies. The next natural progression of DeFi is trust.

The zkVerified Future

Rational privacy is required in DeFi. Trust based on commonly held standards and assertions that are provable. Keyring will remove compliance risk and smart contract risk, opening the floodgates to capital markets.

Prove what matters. Share nothing else. Access everything.

The zkVerified revolution starts now. Institutional money is moving. DeFi will win, verified, not doxxed.

Methodology: Britain has the longest history of markets, allowing us to obtain gold prices back to 1257 in pounds. We converted GBP to USD using historic inflation figures and 2024 GBP/USD rates. Sources: Lawrence H. Officer and Samuel H. Williamson, "The Price of Gold, 1257 - Present," MeasuringWorth, 2025; Eric W. Nye, Pounds Sterling to Dollars: Historical Conversion of Currency.

The future is zkVerified. Join our community to be early for the upcoming TGE: https://t.me/keyring_network

Here's the thing about DeFi onboarding - it sucks.

Users want to prove they're legitimate humans without doxxing themselves. Protocols want verified users without the liability of storing passport photos. Everyone's stuck with slow, clunky verification that makes people abandon ship halfway through.

30-60 second wait times might not sound like much, but in crypto, that's an eternity. Users bounce. Platforms lose customers. The whole "verified, not doxxed" promise feels broken when the tech can't keep up.

Time to fix that.

What We Fixed: Speed + Trust + Compatibility

Our latest Keyring Connect upgrade tackles three critical areas:

Faster Verification

• Notarisation times slashed from 30-60 seconds to 15-30 seconds

• Significantly improved user experience and conversion rates

• Eliminated timeouts for users with slower connections

Enhanced Contextual Integrity

• Bulletproof verification of claims within their full context

• Zero tolerance for malicious data injection attempts

• Enterprise-grade fraud prevention for sensitive applications

Expanded API Compatibility

• Native support for chunked transfer-encoding

• Broader exchange and platform integrations

• Cleaner, more maintainable verification pipeline

Technical Deep Dive

TLS Notary Alpha 10: The Game-Changer

The core performance improvement comes from TLS Notary alpha 10's architectural overhaul. The previous alpha 5 implementation relied heavily on an "online" phase where the client, notary, and API server interacted in real-time, constructing the TLS connection using garbled circuits. This was inherently latency-sensitive - slow connections to our notary could drag the entire process down, occasionally causing timeouts.

Alpha 10 shifts much of this computational work to an "offline" phase, where the client and notary handle cryptographic proofs without tying up the API connection. This dramatically improves reliability and reduces the impact of network latency on verification times.

Quicksilver Proving System: The offline phase now uses Quicksilver, a proving system based on Vector Oblivious Linear Evaluations (VOLE). Compared to the previous JKO13 garbled circuits, Quicksilver delivers substantially faster proof generation - like upgrading from dial-up to fiber for cryptographic operations. Combined with alpha 10's round optimizations in the MPC engine, this creates a much smoother user experience.

MPC-TLS Architecture: We continue to use Multi-Party Computation TLS over alternatives like ZK-TLS because it's fundamentally more censorship-resistant. Unlike centralized proxy solutions that exchanges could block, MPC-TLS permits direct or flexibly-proxied connections, ensuring platforms and users can always trust the verification process.

Keyring's Security Enhancements

Beyond the TLS Notary upgrade, we've implemented significant security improvements:

Contextual Integrity: Our verification now requires users to reveal all HTTP headers and the complete JSON response structure (keys without sensitive values). This prevents malicious actors from injecting fake data into unexpected fields or headers that could fool our verifier. We enforce verification of claims within their known JSON path in the authenticated API response, ensuring every claim is legitimate.

Think of it like checking a passport - we don't just verify the name, we check the issuer, format, security features, and context. A claim of "verified: true" is only meaningful when we can prove it came from the right field, in the right response, from the right API endpoint.

Enhanced API Compatibility: Some exchange APIs use chunked transfer-encoding, which TLS Notary's built-in parsing couldn't handle cleanly. This forced us into workarounds that were harder to maintain and potentially weakened our contextual integrity checks.

Our solution adds native chunked encoding support and enhances TLS Notary's utilities to automatically enforce contextual integrity. This means cleaner integrations with complex APIs and more robust verification across different data sources.

Why This Matters for DeFi's Future

Fast verification isn't just a nice-to-have - it's make-or-break for mainstream adoption.

For Protocols: Higher user conversion, lower bounce rates, institutional-ready compliance without data liability.

For Users: Prove you're legitimate in seconds, keep your privacy intact, access the same opportunities as institutions.

For the Ecosystem: This is how DeFi scales without becoming TradFi. Verified access that preserves crypto's core values.

What's Next

We're not stopping here. Alpha 11 integration is already planned, new data sources are coming, and verification times will keep dropping.

Fast, anonymous, bulletproof verification. That's how we bridge institutional capital and user privacy. That's how DeFi grows up without selling out.

Ready to integrate verified access into your protocol? Keyring Connect takes 3 hours to set up and 3 minutes for users to verify. No passport selfies required.

Read on X:

Keyring Network

@KeyringNetwork

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4:23 PM • Jul 18, 2025

Learn more: keyring.network
Integration docs: docs.keyring.network
Contact: contact@keyring.network

The End of Passport Selfie Theater

To understand why this SEC shift matters, we need to remember where DeFi was just months ago. Operation Chokepoint 2.0 nearly killed the industry in America. The FDIC sent "pause letters" to 23 banks telling them to halt crypto activities. Banks like Signature and Silvergate collapsed under regulatory pressure. Coinbase faced SEC lawsuits. DyDx moved offshore. Rari shut down. OpenSea and Lido got hit with Wells notices. The FRAX founder Sam Kazemian had his JPMorgan accounts closed after being told "we have to close anyone's account that we know their primary source of income/wealth is crypto". Gary Gensler's SEC launched case after case and lost virtually every major one, including Grayscale, partial losses in Ripple, and failed asset freezing attempts against Binance.

DeFi protocols were caught in an impossible position: operate in complete openness (risking regulatory action) or implement traditional KYC that fundamentally broke the user experience with document uploads, passport selfies, and weeks-long approval processes.

The SEC's shift recognizes what we've known all along: requiring someone to upload their passport to access a smart contract doesn't make DeFi safer, it just creates busywork that looks like compliance without actually providing security.

Real safety comes from knowing your users are legitimate humans, not from collecting their government IDs in a centralized database that becomes a honeypot for hackers. Just ask the 69,000+ Coinbase users whose personal data, government IDs, and financial information were stolen by hackers who bribed overseas support staff.

No More Bullshit Tickbox Compliance

Here's what the SEC's "innovation exemption" really means:

• No more bullshit tickbox compliance at the protocol level

• Developers can't be held responsible for how their open-source code is used

• Focus shifts to actual user safety rather than paperwork collection

Removing traditional KYC requirements doesn't eliminate the need for trust. Institutions still need to know their counterparties aren't criminals or malicious actors. Protocols still need to stop industrial farmers gaming their systems. Communities still want fair governance.

This is exactly the gap Keyring Connect was built to fill.

Verified, Not Doxxed

While traditional compliance asks "Can you prove who you are?", Keyring Connect asks the better question: "Can you prove you're eligible?"

Instead of passport selfies, users prove verification status through their existing web2 accounts. Think of it like showing your Binance verification without revealing any personal details – you get access based on eligibility, not identity.

The beauty of zero-knowledge proofs means users can satisfy institutional requirements while maintaining the privacy and sovereignty that drew them to DeFi in the first place.

DeFi's 1970s Moment Has Arrived

As we've written before, DeFi is retracing TradFi's evolution at breakneck speed. We've seen the emergence of money (Bitcoin), credit (MakerDAO), markets (Uniswap), and derivatives (Synthetix) – all following the same sequence as traditional finance, just compressed into decades instead of centuries.

Now we've reached DeFi's 1970s moment: the compliance inflection point. Just as the Bank Secrecy Act and KYC rules allowed traditional markets to scale systematically in the 1970s, DeFi needs its own trust infrastructure to unlock institutional capital.

The SEC's regulatory evolution creates the perfect conditions for this breakthrough. Protocols can innovate freely without compliance overhead, institutions get verified counterparties without compromising user privacy, and users maintain sovereignty while accessing institutional-grade yields.

This isn't just about regulatory relief, it validates our core thesis that DeFi's next phase requires privacy-preserving trust infrastructure. The trillions in institutional capital sitting on the sidelines aren't waiting for less regulation; they're waiting for better verification systems that respect both compliance needs and crypto's core values.

Traditional KYC creates a binary choice: privacy or access. zkVerified pools eliminate this false choice entirely, letting users prove eligibility without revealing identity. Think of it as DeFi's parallel to how Eurodollars unlocked offshore liquidity in traditional finance – verified access becomes the bridge that brings institutional capital onchain without breaking DeFi's foundational principles.

Take zkVerified pools as an example, users could potentially access institutional-grade lending that beats traditional savings rates, all without sharing personal data. No passport uploads. No weeks-long approvals. No wallet-to-identity links. Just cryptographic proof that they're legitimate participants.

Stay anonymous, stay in control, still get in.

The SEC's innovation exemption goes beyond mere regulatory relief, it's recognition that DeFi's unique architecture requires unique solutions. We're moving beyond the false choice between "wild west" openness and "traditional finance" surveillance.

While others scramble to understand what post-exemption compliance looks like, we've been building it. DeFi is growing up, doesn't mean putting on a tie.

• Universal verification across any HTTPS-enabled platform

• Cross-chain compatibility for multichain DeFi

• Privacy-preserving trust that actually scales

The regulatory landscape just caught up to our vision. The future of DeFi is verified, not surveilled.

Learn more about Keyring Connect at keyring.network.

• Depositors in DeFi provide USD liquidity.

• Borrowers use that liquidity to fund leveraged crypto basis trades — typically the difference between perpetual swaps and spot prices across exchanges.

This means that DeFi borrow rates are determined by two supply/demand dynamics:

• The demand for crypto leverage (e.g. via perps), and

• The availability of capital from DeFi depositors.

Compliance and Insurance

For DeFi lending to achieve mass adoption and rival traditional markets, it must overcome two critical barriers: compliance and operational risk. Emerging solutions are paving the way, but widespread adoption of the following is essential:

• Compliance infrastructure (e.g. Keyring) to ensure verifiable, compliant participants.

• Regulated insurers to underwrite DeFi-specific operational risks at scale.

with these solutions in place, DeFi depositors face a simplified risk landscape:

• Rates Risk

  • Borrowing demand: Demand for USD against BTC/ETH collateral drives utilisation.

  • Funding rate: The cost/benefit of going long crypto through perpetual swaps.

• Gap (jump-to-default) Risk: Collateral value crashing faster than liquidation processes can cover.

• Liquidity Risk: Inability to exit if dominating a pool at high utilisation.

As concentration and rates risks are standard in functioning capital markets, gap risk emerges as the primary concern.

The Residual "Gap Risk"

With compliance and operational insurance solved, the primary remaining risk is gap risk.

Gap Risk Manifested in credit models as the Jump-to-Default risk: The risk that collateral value crashes faster than liquidation mechanisms can respond.

In highly volatile events (e.g. a 30–50% crash in minutes), over-collateralised loans can still default.

However, with conservative Loan-To-Value (LTV) settings, automated liquidations, and insurance backstops, this risk becomes manageable and quantifiable, similar to how volatility risk is priced in options markets.

Could DeFi Lending Pull Capital From Sovereign Debt?

If DeFi deposits:

• were properly insured and compliant

• consistently offered yields higher than central bank debt (government bonds)

Even with some embedded gap risk, there could be a gradual reallocation of capital. European Government Bonds (EGBs), yielding* 2–4% and T-bills yielding ~4.3% could face marginal outflows toward DeFi lending opportunities yielding 6-10%.

This wouldn’t be a wholesale shift because systemic players (banks, insurers, pensions) remain tied to regulatory capital rules. But at the margin, flexible allocators, HNWIs, family offices, corporates, would move.

In fact, we are already seeing the beginnings of this in DeFi markets where groups like the TurtleClub are coordinating efforts from HNWI, Family Offices and Treasuries into such lending markets.

At present this seems to be viewed more like an alternative to private credit, due to the various, and significant, compliance and operational risks inherent but the removal of credit risk via smart contracts is extremely attractive. The DeFi rates markets, are also shielded from the direct effects of inflation mis-management and political risk. Political risk remains tied to regulators for now but the tailwinds are positive and significant so we can assume this risk will decrease dramatically, at least over the next 3-4 years.

*DE10y @ 2.535%; IT10y @ 3.637% and US10y @ 4.31% as of 3-May-25

DeFidollars: A Historical Parallel to Eurodollars

Just as Eurodollars quietly shifted monetary power from national governments to market participants, DeFi lending could shift credit creation toward decentralised, crypto-collateralised systems over time.

The following excerpt, a 1971 article in the New York Times, captured how the Eurodollar system had already begun to operate like a recursive loop — not unlike leveraged strategies in DeFi today:

A foreign business — say, West German manufacturer— earns dollars by exporting goods to the United States. The business turns those dollars over to a German commercial bank, which exchanges them for marks at the Bundesbank, the West German central bank.

The Bundesbank adds those dollars to its reserves and then deposits the dollars with the Bank for International Settlements in Basel, Switzerland, to earn interest on them.

The B.I.S. lends the dollars it acquires in the Eurodollar market - say, in London - in order to earn interest to cover its interest obligations to the Bundesbank or other central banks. 

The Eurodollars offered by the B.I.S. in London are borrowed by an Italian concern that pays them to a German company, which puts them in a German commercial bank. That bank exchanges the dollars once more for marks at the Bundesbank. And the Bundesbank adds the dollars to its re serves for the second time. 

This recursive dollar loop exemplified how the Eurodollar system operated in practice

• Eurodollars were offshore U.S. dollar deposits, outside U.S. regulation.

• They created a parallel dollar liquidity system, governed by market forces rather than central banks.

• Over time, the Eurodollar market grew larger than the domestic U.S. deposit base and shaped global liquidity.

A similar pattern is re-emerging in what might be termed DeFidollars, where stablecoins and crypto-collateral drive a recursive, extraterritorial dollar credit system that bypasses traditional regulations to enable market-driven credit creation through decentralised, crypto-collateralised mechanisms.

A parallel funding market

The important difference is that DeFi lending would be fully collateralised, transparent, and programmatically enforced, whereas Eurodollar markets were opaque and unsecured.

Unlike the opaque, unsecured Eurodollar markets, DeFi lending’s transparency and programmatic enforcement could amplify its disruptive potential.

How did Eurodollar participants make money?

Building on the DeFidollar parallel, where market-driven systems bypass traditional controls, Eurodollar participants profited by exploiting structural asymmetries, strategies that DeFi platforms can replicate:

Regulatory Arbitrage

Eurodollar deposits avoided U.S. capital controls, reserve requirements, and deposit insurance costs. Banks could lend USD offshore at higher rates than they paid depositors, sidestepping U.S. regulation entirely.

DeFi Parallel Decentralised lending apps operate outside Basel rules, using smart contracts to offer dollar-denominated loans collateralised by crypto, capturing spreads without a banking license.

Spread Capture and Intermediation

Eurodollar dealers profited from pricing liquidity risk, especially in an opaque, bilateral interbank system. Access to dollar liquidity, especially in times of stress, commanded a premium.

DeFi Parallel DeFi lenders earn yields by pricing basis trade demand, with liquidity providers and vault creators manufacturing synthetic credit products, mirroring Eurodollar brokers’ offshore loan pricing.

Leverage

Offshore banks reused Eurodollar deposits as reserves, creating more USD credit than existed in the U.S. system. Chains of interbank loans generated shadow leverage far beyond actual reserves, unchecked by central banks.

DeFi Parallel Recursive lending loops, leveraged vaults, and liquid staking derivatives build credit pyramids atop crypto collateral, offering full on-chain transparency unlike Eurodollar opacity.

Creation of a Parallel Yield Curve

The Eurodollar market birthed LIBOR, a global benchmark rate set by market participants, not central banks.

DeFi Parallel Lending protocols like Pendle shape crypto-native yield curves, pricing time and risk independently of state control.

These mechanisms highlight how Eurodollar markets thrived on regulatory and structural gaps, a playbook DeFidollar markets are adapting with greater transparency and automation. Yet, significant challenges remain for DeFi to scale these opportunities.

Headwinds and Challenges

While DeFidollar markets offer significant profit potential, scaling these opportunities requires overcoming key challenges:

• Gap Risk Management: The DeFi lending landscape is nascent in risk management practices. At present, and to our knowledge, no lending market has published its analysis on collateral limits with any sort of formal rigour. There needs to be a dramatic upgrade in the market risk management frameworks.

• Liquidity Crunch Events: Sudden liquidity vacuums could still cause temporary disruptions.

• Regulatory Retaliation: Governments could seek to restrict or heavily regulate large-scale DeFi lending activity if it threatens sovereign funding models.

• Scaling Challenges: Deploying hundreds of billions into DeFi lending without yield collapse or system stress will require serious scaling of infrastructure and risk management tools.

What Next?

To profit from the DeFidollar revolution, focus on owning the bottlenecks where value accrues, such as:

• Programmatic Credit Issuance: Create automated, transparent lending systems that issue crypto-collateralised credit at scale, capturing spreads without regulatory overhead.

• Yield Curve Creation: Develop protocols or platforms that shape DeFi-native yield curves, to price risk and time independently of traditional finance.

• Risk and Compliance Infrastructure: Build tools, like Keyring, to ensure verifiable identities, manage gap risk, and integrate regulated insurance for operational stability.

With transparency, automation, and smart collateral as core strengths, DeFidollars could forge a more resilient shadow banking system than Eurodollars. Just as the Eurodollar market enriched intermediaries who mastered its asymmetries, the Defidollar era will reward those who dominate issuance, risk management, and infrastructure.

DeFi isn’t reinventing finance, it’s retracing TradFi’s evolution on a compressed timeline.

Coinage, credit, markets, derivatives: each DeFi primitive mirrors a historical milestone. Now, just like TradFi in the 1970s, crypto faces its compliance moment. Trust infrastructure, like emerging identity and compliance tools, is the inevitable next step to scale capital without compromising principles.

A Familiar Path, Accelerated

Morpheus’ words, underscore the natural progression of financial systems, a path both TradFi and DeFi have traced.

Finance has long evolved by fostering trust, expanding access, and simplifying complexity—from Mesopotamia’s coins to global markets, each step addressed a new challenge. Crypto accelerates this journey: Bitcoin established digital currency, MakerDAO pioneered permissionless lending, Uniswap opened global markets, Synthetix brought derivatives, and Pendle innovated yield strategies.

Like a system following the path of least action in physics, financial primitives emerge in a logical sequence driven by necessity rather than chance.

A Timeline of (De)Financial Evolution

It is remarkable that for each significant financial primitive, both the TradFi and DeFi equivalents became popularised in the same ordering. Looking at the dates each major protocol became a unicorn is an unbiased way of showing this relationship. The only major missing primitives are under-collateralised credit and insurance.

• 2000-600 BC: Coinage [DeFi: 2013-2018]
  TradFi: Coins in Mesopotamia and Lydia standardised trade, replacing barter with trusted money.
  DeFi: Bitcoin created a verifiable, decentralised digital currency, bypassing central banks.

  • Mar-2013 Bitcoin becomes a unicorn

  • May-2016 Ethereum becomes a unicorn

• 1200-1500s: Banking and Credit [DeFi: 2018-2020]
  TradFi: Over-collateralised loans and promissory notes enabled trade beyond physical coins.
  DeFi: MakerDAO’s DAI and AAVE’s pooled liquidity (aTokens) made credit permissionless and composable.

  • Jan-2018 Maker becomes a unicorn

  • Nov-2020 AAVE becomes a unicorn

  

  Note that this period, in DeFi terms, was rather extended due to the 2018 round tariffs, followed by Powell's comments “We’re a long way from neutral at this point, probably.” causing a sell off in the last quarter of 2018. Finally, even though markets had a 'Goldilocks' period through 2019, this was welcomed by the largest pandemic of modern times at the end of the year, disrupting markets until late 2020.

• 1600s-1800s: Capital Markets [DeFi: early 2021]
  TradFi: Stock markets and corporate bonds funded industrial growth. Central banks like Bank of England/Bank of Amsterdam provide asset custody (gold, securities etc.).
  DeFi: Uniswap’s 24/7 exchange. AAVE begins listing many "Altcoins" as collateral. FireBlocks MPC tech provides institutional self-custody.

  • Dec-2020 AAVE lists CRV as collateral. BAL & SUSHI follow

  • Jan-2021 Uniswap becomes a unicorn

  • Mar-2021 FireBlocks raise 133m (assuming 20% dilution and 5x token multiplier this makes Fireblocks a unicorn. They raised another true PE unicorn in December.)

• 1900s: Derivatives and Synthetics [DeFi: late 2021]
  TradFi: Futures, options, and mortgage-backed securities created layered risk strategies.
  DeFi: Synthetix derivative infrastructure and Securitize’s tokenised assets (RWAs) enable hedging and structured yield.

  • Jun-2021 Securitize raised 48m from MS (assuming 20% dilution and 5x token multiplier this makes Securitize a unicorn on a token equivalent basis)

  • Jul-2021 Synthetix becomes a unicorn

  

  This DeFi period faced severe disruption after the pandemic. COVID-era QE fuelled a spending glut, sparking rampant inflation and the fastest rate-hiking cycle in decades as central banks struggled to regain control. The fallout hit crypto markets hard: over-leveraged prime brokers and projects collapsed, culminating in FTX’s downfall. The Democrats, eager to distance themselves from a disgraced crypto industry, launched an unprecedented anti-industry crackdown nicknamed Operation Chokepoint 2.0. This, combined with the market’s implosion, stalled DeFi market growth for nearly two years.

• 1940s-1960s: Retail and Liquidity [DeFi: 2024-2025]
  TradFi: Mutual funds and credit cards brought finance to the masses, with Eurodollars creating offshore liquidity. Zero-coupon bonds gained popularity, offering yield curve exposure. Money Market Funds (MMFs) became mainstream.
  DeFi: Ondo built on Securitize and Blackrock's foundations to release the first major institutional MMF. Ethena creates the first major mutual fund, while stablecoins act as DeFi’s global liquidity engine. The new payments cards are technically short term credit cards but DeFi still struggles to capture a true retail credit card facility. Pendle captured the market of yield stripping.

  • Mar-2024 Ondo becomes a unicorn

  • Apr-2024 Ethena launches at >$1bn valuation

  • Apr-2024 Pendle becomes a unicorn

  • Apr-2024 1Inch launches 1Inch Card in cooperation with Mastercard

  • May-2024 Gnosis Pay powers a unicorn valuation for Gnosis in cooperation with Visa

• 1970s: Compliance and Trust [DeFi: expected late 2025-2026]
  TradFi: The Bank Secrecy Act and KYC rules gave markets ability to systematically scale under clear guidance. Negotiable Certificate of Deposits enabled banks to issue debt of varied duration to generate yield from Asset & Liability Management (ALM). Mortgage-Backed Securities (MBS) and Private Equity (PE) funds emerged.
  DeFi: Keyring Network creating trust infrastructure for compliance and security, unlocking institutional capital. InfiniFi are creating duration-driven yield with decentralised ALM.

  

  The current DeFi market is contending with macro headwinds, notably the renewal of tariffs and growing bond market concerns over the Federal Reserve’s independence. As in 2018, the hope is that the U.S. administration will move away from protectionist rhetoric, creating space to ease pressure on global markets and de-escalate tensions with the FED.

  There is a strong chance that the FED will hold off until at least Q3, both as a signal of institutional independence and, more critically, due to inflation risks. The University of Michigan Inflation Expectations index has surged from 2.6% to 5% since November 2024, echoing conditions in 2022 when the Fed adopted a hawkish stance in response to similar consumer sentiment data.

  

• 1980s: Swaps, Diversification and AML [DeFi: expected 2026/7]

  TradFi: Swaps enabled risk management via yield curves. CDOs pooled debt, amplifying returns and risks. ETFs offered retail diversification. FATF set global AML standards.
  DeFi: Lending apps figure out how to popularise over-collateralised swap rates. Synthetic assets tranche debt like CDOs. DeFi ETFs pool crypto for retail. On-chain compliance aligns with FATF standards.

• 1990s: Financial Engineering [DeFi: expected 2027/8]

  TradFi: REITs enabled retail real estate investment. Credit Default Swap (CDS) hedged credit risk. Structured products, driven by derivative pricing, offered tailored returns. Algorithmic trading. Travel Rule become adopted.
  DeFi: Tokenised real estate mimics REITs. Decentralised CDS might be tricky due to balance sheet requirements. Structured yield products use DeFi derivatives. Algo-bots optimise pools. On-chain AML enforces something that seems similar to Travel Rule.

Beyond the 1990s [DeFi: 2028], DeFi projections become too speculative, so we stop here.

Building Pressure for a Breakthrough

Crypto’s recent growth has been fuelled by consumer speculation, with platforms like pump.fun earning $600M in fees since January 2024. Yet, DeFi’s potential for composable markets and scalable capital is under pressure. Off-chain credit and identity challenges, especially in under-collateralised lending, create barriers. Compliance, identity, and trust gaps hinder anti-fraud and credit screening, delaying progress.

This tension is building toward a pivotal shift. Robust compliance infrastructure like, Keyring's verified markets, is emerging, redirecting focus to on-chain DeFi solutions. The result will be rapid and massive credit adoption, transforming the landscape. As DeFi matures along its historical timeline, it’s ready for a breakthrough.

Also, the rates market is poised to lead TradFi adoption in this new era. Here, DeFidollars (a parallel funding market of stablecoins and crypto-collateral, echoing Eurodollars) enable verifiable lending and borrowing. This marks the first wave of a seismic change which we will explore in our next post.

Embracing a Global Dawn

Crypto’s evolution follows a deterministic path, not by chance but by necessity, like a system optimising for efficiency. Each TradFi milestone: coinage, credit, derivatives, compliance, has a DeFi counterpart, emerging in sequence.

Now, at the compliance stage, crypto can scale to rival global markets. Emerging trust infrastructure is building the foundation for this leap: private identities enable secure access, composable compliance ensures regulatory alignment, and trustless systems unlock institutional capital. Imagine a DeFi ecosystem where a small business secures a loan via a compliant, decentralised platform, or an investor trades tokenised assets with verified identity, all without central intermediaries.

DeFi is not stuck in the 1970s, it’s poised to usher in a global era, redefining finance for the future.

Imagine if you could buy an asset or receive an airdrop without having to upload your passport for the millionth time. No endless onboarding loops. No anxiety about leaving sensitive information behind, worrying it might be hacked or sold. As for platforms, no more losing half your audience during onboarding and frustrating the other half.

That’s what Keyring solves. Most of us are used to giving away privacy and data ownership to save a few clicks and "login with" our Google or social media accounts. What if we could use a similar approach to prove we're legit KYC'd users? Proving we’ve already been verified by our bank, broker or centralised exchange. Yet this time, not at the expense of privacy and data ownership.

Another way to “know your customer”

For blockchain-based systems to go mainstream, we need solutions that prevent interactions with bad actors. That doesn’t mean we’re stuck with the cumbersome KYC processes we know today.

Financial institutions have spent decades refining their onboarding and fraud prevention systems. They still collectively invest over $200 billion each year. With AI providing increasingly sophisticated methods to outsmart existing KYC systems, even more investments are required to keep up. Institutions must also safeguard sensitive user data, and even the most secure systems are regularly breached.

So why don't we rely on these trusted checks rather than having every platform re-verify users, and store personal information at great risk?

Let’s be clear, this requirement won’t disappear overnight. Most regulators require financial service providers to verify and store user information, so they can be held accountable. But if we go back to the concept of “knowing your customer”, using the whole trail of existing reliable information makes a lot of sense. We’d supplement already-verified data from trusted institutions (like KYC passed) with other reputation-based information (like account longevity or transaction history).

While we are not there yet for regulated entities, Keyring Connect offers a solution for all use cases that don't require traditional collect-verify-hold user docs.

Why would these non-regulated use cases implement any form of KYC? Because it still fulfils the underlying purpose: risk mitigation. Today, many platforms skip verification entirely to avoid the heavy UX burden, missing out on the opportunity to tap into huge pools of already-verified users.

If we take the example of lending pools, you can’t scale a pool by manually verifying every participant. The operational hurdles and UX impact would be massive. However, say users can prove their status with Binance or Revolut in a few clicks, your pool has much better chances to attract institutional capital while maintaining a seamless user experience.

This is KYC reliance 2.0. Risk-focused instead of identification-focused. Fully automated. Relying on trustworthy data. The best part: it comes with minimal friction for users and protocols.

Introducing Keyring Connect: Instant zk-KYC

Keyring Connect lets users extract information from any website, with a proof of authenticity.

For instance, you can prove you’ve passed KYC with Revolut or sent a wire transfer. You can show your Coinbase balance or Experian credit score — or simply prove it’s above a certain number.

It’s like open banking but for all data and all websites, without having to build and maintain hundreds of API connections. Unlike scraping the web, data can be collected behind login walls. Users verify themselves once with a trusted platform and carry their credentials across ecosystems. All in just three clicks and under three minutes.

At its core, Connect relies on zkTLS technology, or more precisely MPC-TLS. Built on TLS (the internet’s security layer), it allows a neutral “notary” service to witness a browsing session and prove that the data extracted from the website is authentic. Importantly, zkTLS is built in such a way that only the required information is extracted, ensuring minimum data exposure. It's similar to a blacked-out report. When setting up a new website data source, Keyring uses its URL and JSON path to identify the fields to consider, based on what’s required for a given policy. Data is only shared with user intent.

Think of it as a cryptographically sealed screenshot of selected data points that cannot be tampered with or faked.

Let’s take the example of Alice who wants to lend in a pool. For the pool’s smart contract to accept the transaction, she needs to prove she’s over 18 and doesn’t live in the UK.

1. Data Extraction: Alice opens the Keyring web extension, selects Binance as her data source, and logs in. The extension securely gathers authorised cookies from the session, containing the minimum required data (here: date of birth and address). The Keyring notary authenticates the session, ensuring data integrity.

2. Data Validation: Extracted data is automatically validated against the pool's Policy (set of rules defined by the Keyring customer). For Alice, this means verifying her age is above 18 and her address isn’t in the UK. No external vendor or manual workforce is required. ‍

3. On-Chain Credential Issuance: Once validated, Alice binds her wallet to the verified data, hiding that link with zero-knowledge. This ensures there is no honeypot of information mapping users’ identities to the wallets they use. She then creates an on-chain credential attesting she complies with the policy. Smart contracts dynamically query credentials to approve actions like deposits or trades, making real-world user data accessible on-chain without exposing sensitive details.

On the protocol side, we made integration extra simple and achievable in under 3 hours:

1. Define your compliance Policy with Keyring, specifying the rules and data sources you accept.

2. Include a link or button (via SDK) on your frontend for users to start the verification.

3. Add a modifier to the desired smart contract functions to check the credentials cache before approval. Done.

Our approach is use-case first: we don’t want to distract builders from their core purpose. So we built modular tooling that can be used as a turnkey solution. All protocols can now mitigate risks without prohibitive costs, expand their product offering and attract compliance-conscious capital.

Where is it most useful?

Keyring Connect was built to unlock the potential of digital assets by enabling the secure sharing of sensitive data within crypto ecosystems. The first focus is compliance for DeFi.

We've mentioned permissioned pools in DeFi already. We believe this is the best tool to push institutions over the line and deploy at scale. By ensuring only KYC’d users from non-sanctioned countries can interact with pools, Keyring Connect mitigates risk while respecting the ecosystem’s privacy ethos. It helps break the barrier between retail and institutions in DeFi. As a side benefit, it helps prevent scams and improve hack recovery by providing identity trails and jurisdictions of reference. Idle Finance, Euler and Benqi are already on board, with more coming soon. See it in action on Pareto!

Other applications include attestation layers for blockchain ecosystems, airdrops with geographic restrictions or sybil protection, non-custodial wallet compliance with MiCA travel rule, or enabling AI agents to KYC counterparts. Keyring Connect works everywhere private authenticated data is needed.

We’ll be doing deep dives on these use cases in future articles. Follow us to stay tuned (links at the end).

What next?

Keyring Connect isn’t just about better KYC — it’s about redefining identity verification for decentralised systems. Here’s why it stands out:

1. Quick and painless

2. Respects privacy

3. More secure than sharing docs with every platform

4. Reliable with zkTLS authenticity

5. Universal: works with all websites and data points

6. Fully automated with built-in data refresh

7. Easy to integrate

With AI and decentralisation, the demand for scalable, secure identity solutions will grow. By eliminating the need for human intervention, Keyring Connect offers a level of scalability and efficiency that legacy solutions can’t match. We aim for it to become the best automation tool for identity, to build trust in the programmable economy.

We’re live on Ethereum, Base, Arbitrum, Optimism, Avalanche. Deploying on Midnight, Injective, ZKsync and Solana. Reach out to get started today info@keyring.network.